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Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models

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This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.

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Bibliographic Details
Main Author: Scheubner, Stefan (auth)
Format: Electronic Book Chapter
Language:English
Published: Karlsruhe KIT Scientific Publishing 2022
Series:Karlsruher Schriftenreihe Fahrzeugsystemtechnik 6
Subjects:
Mechanical engineering & materials
Elektromobilität
Vorhersagen
Algorithmen
Fahrzeugtechnik
Energiemanagement
E-Mobility
Forecasting
Algorithms
Vehicle Technology
Energy Management
Online Access:OAPEN Library: download the publication
OAPEN Library: description of the publication
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Description
Summary:This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
Physical Description:1 electronic resource (192 p.)
ISBN:KSP/1000143200
9783731511663
Access:Open Access

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